Integrated computational and Drosophila cancer model platform captures previously unappreciated chemicals perturbing a kinase network

Abstract

Drosophila provides an inexpensive and quantitative platform for measuring whole animal drug response. A complementary approach is virtual screening, where chemical libraries can be efficiently screened against protein target(s). Here, we present a unique discovery platform integrating structure-based modeling with Drosophila biology and organic synthesis. We demonstrate this platform by developing chemicals targeting a Drosophila model of Medullary Thyroid Cancer (MTC) with disease-promoting kinase network activated by mutant dRetM955T. Structural models for kinases relevant to MTC were generated for virtually screening to identify initial hits that were dissimilar to known kinase inhibitors yet suppressed dRetM955T-induced oncogenicity. We then combined features from the hits and known inhibitors to create a hybrid molecule with improved dRetM955T phenotypic outcome. Our platform provides a framework to efficiently explore novel chemical spaces, develop compounds outside of the current inhibitor chemical space, and correct cancer-causing signaling networks to improve disease prognosis while minimizing whole body toxicity.\n\nAUTHOR SUMMARYEffective and safe treatment of multigenic diseases often involves drugs that modulate whole systems by interacting with multiple nodes in pathways and networks, i.e., polypharmacology. Polypharmacology is increasingly appreciated as a potential desirable property of kinase drugs; however, most known drugs that interact with multiple targets have been identified as such by chance, and most polypharmacological compounds are not chemically unique resembling to structures of known kinase inhibitors. The fruit fly Drosophila has been established as a robust screening platform that provides an inexpensive, rapid, and quantitative measure of whole animal drug response, complementing computational approaches. We present a chemical genetics approach that efficiently combines Drosophila with structural prediction and virtual screening, creating a unique discovery platform. We demonstrate the utility of our approach by developing useful small molecules targeting a kinase network in a Drosophila model of Medullary Thyroid Cancer (MTC) driven by the active mutant dRetM955T.